MCP Translation Server
@KYBvWHxW
About MCP Translation Server
High-performance Manchu-Chinese translation server implementing the Model Context Protocol (MCP)
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-translation-server": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}Tools
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Overview
What is MCP Translation Server?
MCP Translation Server is a high-performance machine translation system specialized for Manchu-Chinese bidirectional translation, combining advanced linguistic processing and deep learning for low-resource language translation.
How to use MCP Translation Server?
Clone the repository, set up a Python virtual environment (Python 3.12+), install dependencies, copy and edit config.example.py to config.py, and set required environment variables (MCP_SECRET_KEY, MCP_API_TOKEN). Run python server.py to start the server, or run python demo/comprehensive_demo.py for a demo.
Key features of MCP Translation Server
- Enhanced morphological analysis with vowel harmony and error detection
- Multi-level translation engine with intelligent corpus matching
- Rich language resources (rules, parallel corpus, dictionary)
- REST API endpoints for translation and morphological analysis
- Performance: average latency <1s, cache hit rate >80%
- Monitoring via Prometheus and Grafana
Use cases of MCP Translation Server
- Bidirectional translation between Manchu and Chinese
- Morphological analysis of Manchu text (word form, vowel harmony)
- Batch processing of parallel corpus data
- Real-time translation with high concurrency (100+ req/s)
FAQ from MCP Translation Server
What runtime dependencies does MCP Translation Server require?
Python 3.12+ is required. Docker is supported. The server also needs a config.py file and the environment variables MCP_SECRET_KEY and MCP_API_TOKEN (optional: MCP_REDIS_PASSWORD, MCP_SMTP_PASSWORD).
How do I configure the server?
Copy config.example.py to config.py and edit the settings (API host/port, rate limiting, caching, model paths, etc.). Never commit config.py to version control.
What API endpoints are available?
POST /api/v1/translate for translation (with text, source_lang, target_lang) and POST /api/v1/analyze for morphological analysis.
What data does MCP Translation Server use?
It uses bundled language resources: Manchu grammar rules (manchu_rules.json), a parallel corpus (parallel_corpus.json), and a dictionary (dictionary.json).
How is performance measured?
Average translation latency is under 1 second, 95th percentile under 2 seconds, and the server handles over 100 concurrent requests per second.
Frequently asked questions
What runtime dependencies does MCP Translation Server require?
Python 3.12+ is required. Docker is supported. The server also needs a `config.py` file and the environment variables `MCP_SECRET_KEY` and `MCP_API_TOKEN` (optional: `MCP_REDIS_PASSWORD`, `MCP_SMTP_PASSWORD`).
How do I configure the server?
Copy `config.example.py` to `config.py` and edit the settings (API host/port, rate limiting, caching, model paths, etc.). Never commit `config.py` to version control.
What API endpoints are available?
`POST /api/v1/translate` for translation (with `text`, `source_lang`, `target_lang`) and `POST /api/v1/analyze` for morphological analysis.
What data does MCP Translation Server use?
It uses bundled language resources: Manchu grammar rules (`manchu_rules.json`), a parallel corpus (`parallel_corpus.json`), and a dictionary (`dictionary.json`).
How is performance measured?
Average translation latency is under 1 second, 95th percentile under 2 seconds, and the server handles over 100 concurrent requests per second.
Basic information
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